{"title":"A LC Model With Change-Points Regime","authors":"Vered Shapovalov, Z. Landsman, U. Makov","doi":"10.2139/ssrn.3319712","DOIUrl":null,"url":null,"abstract":"This paper extends the widely used Lee Carter (LC) model (Lee & Carter, 1992) for mortality projection. We suggest a Bayesian change-points model for the time parameters in the Bayesian extension of the LC model suggested in Czado et al. (2005). In particular, we modify the simple linear trend to a piecewise linear trend. This model accounts for changes in trend over time and it is inspired by the Bayesian random level{shift model of McCulloch & Tsay (1993). In a validation-based examination, the proposed change-points model produces smaller prediction errors compared to the autoregressive model for the time parameters in Czado et al. (2005). Notably, this is true for all populations considered.","PeriodicalId":260073,"journal":{"name":"Mathematics eJournal","volume":"106 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematics eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3319712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper extends the widely used Lee Carter (LC) model (Lee & Carter, 1992) for mortality projection. We suggest a Bayesian change-points model for the time parameters in the Bayesian extension of the LC model suggested in Czado et al. (2005). In particular, we modify the simple linear trend to a piecewise linear trend. This model accounts for changes in trend over time and it is inspired by the Bayesian random level{shift model of McCulloch & Tsay (1993). In a validation-based examination, the proposed change-points model produces smaller prediction errors compared to the autoregressive model for the time parameters in Czado et al. (2005). Notably, this is true for all populations considered.
本文扩展了广泛使用的Lee Carter (LC)模型(Lee &Carter, 1992)用于死亡率预测。在Czado等人(2005)提出的LC模型的贝叶斯扩展中,我们建议使用贝叶斯变点模型来表示时间参数。特别地,我们将简单的线性趋势修改为分段线性趋势。该模型解释了随时间变化的趋势,其灵感来自于麦卡洛克的贝叶斯随机水平偏移模型。-蔡(1993)。在基于验证的检验中,与Czado等人(2005)的自回归模型相比,所提出的变化点模型对时间参数的预测误差更小。值得注意的是,这对所有被考虑的人群都是正确的。